Learning Goals

Lab Description

We will work with the COVID data presented in lecture. Recall the dataset consists of COVID-19 cases and deaths in each US state during the course of the COVID epidemic.

The objective of this lab is to identify how long after cases increased deaths increased, and after cases decreased deaths decreased, and plot data to demonstrate this

Steps

I. Reading and processing the New York Times (NYT) state-level COVID-19 data

1. Read in the data

## data extracted from New York Times state-level data from NYT Github repository
# https://github.com/nytimes/covid-19-data
## state-level population information from us_census_data available on GitHub repository:
# https://github.com/COVID19Tracking/associated-data/tree/master/us_census_data
# load COVID state-level data from NYT
cv_states <- as.data.frame(data.table::fread("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv"))
# load state population data
state_pops <- as.data.frame(data.table::fread("https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv"))
state_pops$abb <- state_pops$state
state_pops$state <- state_pops$state_name
state_pops$state_name <- NULL
cv_states <- merge(cv_states, state_pops, by="state")

2. Look at the data

  • Inspect the dimensions, head, and tail of the data
  • Inspect the structure of each variables. Are they in the correct format?
dim(cv_states)
## [1] 32562     9
head(cv_states)
##     state       date fips  cases deaths geo_id population pop_density abb
## 1 Alabama 2021-08-29    1 691451  12222      1    4887871    96.50939  AL
## 2 Alabama 2021-07-07    1 552911  11387      1    4887871    96.50939  AL
## 3 Alabama 2020-06-21    1  30021    839      1    4887871    96.50939  AL
## 4 Alabama 2020-06-10    1  21989    744      1    4887871    96.50939  AL
## 5 Alabama 2021-07-03    1 551298  11358      1    4887871    96.50939  AL
## 6 Alabama 2021-09-29    1 794773  14200      1    4887871    96.50939  AL
tail(cv_states)
##         state       date fips cases deaths geo_id population pop_density abb
## 32557 Wyoming 2021-07-04   56 62445    747     56     577737    5.950611  WY
## 32558 Wyoming 2021-01-22   56 50583    571     56     577737    5.950611  WY
## 32559 Wyoming 2020-05-10   56   662      7     56     577737    5.950611  WY
## 32560 Wyoming 2020-12-25   56 42664    373     56     577737    5.950611  WY
## 32561 Wyoming 2021-07-16   56 63523    760     56     577737    5.950611  WY
## 32562 Wyoming 2020-11-30   56 33305    215     56     577737    5.950611  WY
str(cv_states)
## 'data.frame':    32562 obs. of  9 variables:
##  $ state      : chr  "Alabama" "Alabama" "Alabama" "Alabama" ...
##  $ date       : IDate, format: "2021-08-29" "2021-07-07" ...
##  $ fips       : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ cases      : int  691451 552911 30021 21989 551298 794773 40111 63091 148206 627905 ...
##  $ deaths     : int  12222 11387 839 744 11358 14200 985 1265 2506 11765 ...
##  $ geo_id     : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ population : int  4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ...
##  $ pop_density: num  96.5 96.5 96.5 96.5 96.5 ...
##  $ abb        : chr  "AL" "AL" "AL" "AL" ...

3. Format the data

  • Make date into a date variable
  • Make state into a factor variable
  • Order the data first by state, second by date
  • Confirm the variables are now correctly formatted
  • Inspect the range values for each variable. What is the date range? The range of cases and deaths?
# format the date
cv_states$date <- as.Date(cv_states$date, format="%Y-%m-%d")
# format the state and state abbreviation (abb) variables
state_list <- unique(cv_states$state)
cv_states$state <- factor(cv_states$state, levels = state_list)
abb_list <- unique(cv_states$abb)
cv_states$abb <- factor(cv_states$abb, levels = abb_list)
# order the data first by state, second by date
cv_states = cv_states[order(cv_states$state, cv_states$date),]
# Confirm the variables are now correctly formatted
str(cv_states)
## 'data.frame':    32562 obs. of  9 variables:
##  $ state      : Factor w/ 52 levels "Alabama","Alaska",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ date       : Date, format: "2020-03-13" "2020-03-14" ...
##  $ fips       : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ cases      : int  6 12 23 29 39 51 78 106 131 157 ...
##  $ deaths     : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ geo_id     : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ population : int  4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ...
##  $ pop_density: num  96.5 96.5 96.5 96.5 96.5 ...
##  $ abb        : Factor w/ 52 levels "AL","AK","AZ",..: 1 1 1 1 1 1 1 1 1 1 ...
head(cv_states)
##       state       date fips cases deaths geo_id population pop_density abb
## 436 Alabama 2020-03-13    1     6      0      1    4887871    96.50939  AL
## 243 Alabama 2020-03-14    1    12      0      1    4887871    96.50939  AL
## 50  Alabama 2020-03-15    1    23      0      1    4887871    96.50939  AL
## 472 Alabama 2020-03-16    1    29      0      1    4887871    96.50939  AL
## 157 Alabama 2020-03-17    1    39      0      1    4887871    96.50939  AL
## 89  Alabama 2020-03-18    1    51      0      1    4887871    96.50939  AL
tail(cv_states)
##         state       date fips  cases deaths geo_id population pop_density abb
## 32208 Wyoming 2021-11-12   56 107483   1298     56     577737    5.950611  WY
## 32144 Wyoming 2021-11-13   56 107483   1298     56     577737    5.950611  WY
## 32162 Wyoming 2021-11-14   56 107483   1298     56     577737    5.950611  WY
## 31982 Wyoming 2021-11-15   56 108103   1298     56     577737    5.950611  WY
## 32314 Wyoming 2021-11-16   56 108413   1347     56     577737    5.950611  WY
## 32206 Wyoming 2021-11-17   56 108658   1347     56     577737    5.950611  WY
# Inspect the range values for each variable. What is the date range? The range of cases and deaths?
head(cv_states)
##       state       date fips cases deaths geo_id population pop_density abb
## 436 Alabama 2020-03-13    1     6      0      1    4887871    96.50939  AL
## 243 Alabama 2020-03-14    1    12      0      1    4887871    96.50939  AL
## 50  Alabama 2020-03-15    1    23      0      1    4887871    96.50939  AL
## 472 Alabama 2020-03-16    1    29      0      1    4887871    96.50939  AL
## 157 Alabama 2020-03-17    1    39      0      1    4887871    96.50939  AL
## 89  Alabama 2020-03-18    1    51      0      1    4887871    96.50939  AL
summary(cv_states)
##            state            date                 fips           cases        
##  Washington   :  667   Min.   :2020-01-21   Min.   : 1.00   Min.   :      1  
##  Illinois     :  664   1st Qu.:2020-08-05   1st Qu.:16.00   1st Qu.:  32536  
##  California   :  663   Median :2021-01-08   Median :29.00   Median : 149252  
##  Arizona      :  662   Mean   :2021-01-08   Mean   :29.78   Mean   : 391586  
##  Massachusetts:  656   3rd Qu.:2021-06-14   3rd Qu.:44.00   3rd Qu.: 489216  
##  Wisconsin    :  652   Max.   :2021-11-17   Max.   :72.00   Max.   :5024415  
##  (Other)      :28598                                                         
##      deaths          geo_id        population        pop_density       
##  Min.   :    0   Min.   : 1.00   Min.   :  577737   Min.   :    1.292  
##  1st Qu.:  632   1st Qu.:16.00   1st Qu.: 1805832   1st Qu.:   43.659  
##  Median : 2688   Median :29.00   Median : 4468402   Median :  107.860  
##  Mean   : 7227   Mean   :29.78   Mean   : 6433123   Mean   :  422.524  
##  3rd Qu.: 8534   3rd Qu.:44.00   3rd Qu.: 7535591   3rd Qu.:  229.511  
##  Max.   :73614   Max.   :72.00   Max.   :39557045   Max.   :11490.120  
##                                                     NA's   :615        
##       abb       
##  WA     :  667  
##  IL     :  664  
##  CA     :  663  
##  AZ     :  662  
##  MA     :  656  
##  WI     :  652  
##  (Other):28598
min(cv_states$date)
## [1] "2020-01-21"
max(cv_states$date)
## [1] "2021-11-17"

4. Add new_cases and new_deaths and correct outliers

  • Add variables for new cases, new_cases, and new deaths, new_deaths:

    • Hint: new_cases is equal to the difference between cases on date i and date i-1, starting on date i=2
  • Use plotly for EDA: See if there are outliers or values that don’t make sense for new_cases and new_deaths. Which states and which dates have strange values?

  • Correct outliers: Set negative values for new_cases or new_deaths to 0

  • Recalculate cases and deaths as cumulative sum of updates new_cases and new_deaths

# Add variables for new_cases and new_deaths:
for (i in 1:length(state_list)) {
  cv_subset = subset(cv_states, state == state_list[i])
  cv_subset = cv_subset[order(cv_subset$date),]
  # add starting level for new cases and deaths
  cv_subset$new_cases = cv_subset$cases[1]
  cv_subset$new_deaths = cv_subset$deaths[1]
  for (j in 2:nrow(cv_subset)) {
    cv_subset$new_cases[j] = cv_subset$cases[j] - cv_subset$cases[j-1]
    cv_subset$new_deaths[j] = cv_subset$deaths[j] - cv_subset$deaths[j-1]
  }
  # include in main dataset
  cv_states$new_cases[cv_states$state==state_list[i]] = cv_subset$new_cases
  cv_states$new_deaths[cv_states$state==state_list[i]] = cv_subset$new_deaths
}
# Inspect outliers using plotly
p1<-ggplot(cv_states, aes(x = date, y = new_cases, color = state)) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p1)
p1<-NULL # to clear from workspace
p2<-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p2)
p2<-NULL # to clear from workspace
# set negative new case or death counts to 0
cv_states$new_cases[cv_states$new_cases<0] = 0
cv_states$new_deaths[cv_states$new_deaths<0] = 0
# Recalculate `cases` and `deaths` as cumulative sum of updates `new_cases` and `new_deaths`
for (i in 1:length(state_list)) {
  cv_subset = subset(cv_states, state == state_list[i])
  # add starting level for new cases and deaths
  cv_subset$cases = cv_subset$cases[1]
  cv_subset$deaths = cv_subset$deaths[1]
  for (j in 2:nrow(cv_subset)) {
    cv_subset$cases[j] = cv_subset$new_cases[j] + cv_subset$cases[j-1]
    cv_subset$deaths[j] = cv_subset$new_deaths[j] + cv_subset$deaths[j-1]
  }
  # include in main dataset
  cv_states$cases[cv_states$state==state_list[i]] = cv_subset$cases
  cv_states$deaths[cv_states$state==state_list[i]] = cv_subset$deaths
}

5. Add additional variables

  • Add population-normalized (by 100,000) variables for each variable type (rounded to 1 decimal place). Make sure the variables you calculate are in the correct format (numeric). You can use the following variable names:

    • per100k = cases per 100,000 population
    • newper100k= new cases per 100,000
    • deathsper100k = deaths per 100,000
    • newdeathsper100k = new deaths per 100,000
  • Add a “naive CFR” variable representing deaths / cases on each date for each state

  • Create a dataframe representing values on the most recent date, cv_states_today, as done in lecture

# add population normalized (by 100,000) counts for each variable
cv_states$per100k =  as.numeric(format(round(cv_states$cases/(cv_states$population/100000),1),nsmall=1))
cv_states$newper100k =  as.numeric(format(round(cv_states$new_cases/(cv_states$population/100000),1),nsmall=1))
cv_states$deathsper100k =  as.numeric(format(round(cv_states$deaths/(cv_states$population/100000),1),nsmall=1))
cv_states$newdeathsper100k =  as.numeric(format(round(cv_states$new_deaths/(cv_states$population/100000),1),nsmall=1))
# add a naive_CFR variable = deaths / cases
cv_states = cv_states %>% mutate(naive_CFR = round((deaths*100/cases),2))
# create a `cv_states_today` variable
cv_states_today = subset(cv_states, date==max(cv_states$date))

II. Scatterplots

6. Explore scatterplots using plot_ly()

  • Create a scatterplot using plot_ly() representing pop_density vs. various variables (e.g. cases, per100k, deaths, deathsper100k) for each state on most recent date (cv_states_today)
    • Use hover to identify any outliers.
    • Remove those outliers and replot.
  • Choose one plot. For this plot:
    • Add hoverinfo specifying the state name, cases per 100k, and deaths per 100k, similarly to how we did this in the lecture notes
    • Add layout information to title the chart and the axes
    • Enable hovermode = "compare"
# pop_density vs. cases
cv_states_today %>% 
  plot_ly(x = ~pop_density, y = ~cases, 
          type = 'scatter', mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))
# filter out "District of Columbia"
cv_states_today_scatter <- cv_states_today %>% filter(state!="District of Columbia")
# pop_density vs. cases after filtering
cv_states_today_scatter %>% filter(state!="District of Columbia") %>% 
  plot_ly(x = ~pop_density, y = ~cases, 
          type = 'scatter', mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))
# pop_density vs. deathsper100k
cv_states_today_scatter %>% filter(state!="District of Columbia") %>% 
  plot_ly(x = ~pop_density, y = ~deathsper100k, z = ~population,
          type = 'scatter', mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))
# Adding hoverinfo
cv_states_today_scatter %>% 
  plot_ly(x = ~pop_density, y = ~deathsper100k,
          type = 'scatter', mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5),
          hoverinfo = 'text',
          text = ~paste( paste(state, ":", sep=""), paste(" Cases per 100k: ", per100k, sep="") , paste(" Deaths per 100k: ",
                        deathsper100k, sep=""), sep = "<br>")) %>%
  layout(title = "Population-normalized COVID-19 deaths (per 100k) vs. population density for US states",
                  yaxis = list(title = "Deaths per 100k"), xaxis = list(title = "Population Density"),
         hovermode = "compare")

7. Explore scatterplot trend interactively using ggplotly() and geom_smooth()

  • For pop_density vs. newdeathsper100k create a chart with the same variables using gglot_ly()
  • Explore the pattern between \(x\) and \(y\) using geom_smooth()
    • Explain what you see. Do you think pop_density is a correlate of newdeathsper100k?
p <- ggplot(cv_states_today_scatter, aes(x=pop_density, y=deathsper100k, size=population)) + geom_point() + geom_smooth()
ggplotly(p)

8. Multiple line chart

  • Create a line chart of the naive_CFR for all states over time using plot_ly()
    • Use hoverinfo to identify states that had a “first peak”
    • Use the zoom and pan tools to inspect the naive_CFR for the states that had a “first peak” in September. How have they changed over time?
  • Create one more line chart, for Texas only, which shows new_cases and new_deaths together in one plot. Hint: use add_layer()
    • Use hoverinfo to “eyeball” the approximate peak of deaths and peak of cases. What is the time delay between the peak of cases and the peak of deaths?
# Line chart for naive_CFR for all states over time using `plot_ly()`
plot_ly(cv_states, x = ~date, y = ~naive_CFR, color = ~state, type = "scatter", mode = "lines")
# Line chart for Texas showing new_cases and new_deaths together
cv_states %>% filter(state=="Texas") %>% plot_ly(x = ~date, y = ~new_cases, type = "scatter", mode = "lines") %>% add_lines(x = ~date, y = ~new_deaths, type = "scatter", mode = "lines") 

9. Heatmaps

Create a heatmap to visualize new_cases for each state on each date greater than April 1st, 2020 - Start by mapping selected features in the dataframe into a matrix using the tidyr package function pivot_wider(), naming the rows and columns, as done in the lecture notes - Use plot_ly() to create a heatmap out of this matrix - Create a second heatmap in which the pattern of new_cases for each state over time becomes more clear by filtering to only look at dates every two weeks

# Map state, date, and new_cases to a matrix
library(tidyr)
cv_states_mat <- cv_states %>% select(state, date, new_cases) %>% filter(date>as.Date("2020-04-01"))
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = new_cases))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)
# Create a heatmap using plot_ly()
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
             z=~cv_states_mat2,
             type="heatmap",
             showscale=T)
# Create a second heatmap after filtering to only include dates every other week
filter_dates <- seq(as.Date("2020-04-01"), as.Date("2020-10-01"), by="2 weeks")
cv_states_mat <- cv_states %>% select(state, date, new_cases) %>% filter(date %in% filter_dates)
cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = new_cases))
rownames(cv_states_mat2) <- cv_states_mat2$date
cv_states_mat2$date <- NULL
cv_states_mat2 <- as.matrix(cv_states_mat2)
# Create a heatmap using plot_ly()
plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),
             z=~cv_states_mat2,
             type="heatmap",
             showscale=T)

10. Map

  • Create a map to visualize the naive_CFR by state on May 1st, 2020
  • Compare with a map visualizing the naive_CFR by state on most recent date
  • Plot the two maps together using subplot(). Make sure the shading is for the same range of values (google is your friend for this)
  • Describe the difference in the pattern of the CFR.
### For May 1 2020
# Extract the data for each state by its abbreviation
cv_CFR <- cv_states %>% filter(date=="2020-05-01") %>% select(state, abb, naive_CFR, cases, deaths) # select data
cv_CFR$state_name <- cv_CFR$state
cv_CFR$state <- cv_CFR$abb
cv_CFR$abb <- NULL
# Create hover text
cv_CFR$hover <- with(cv_CFR, paste(state_name, '<br>', "CFR: ", naive_CFR, '<br>', "Cases: ", cases, '<br>', "Deaths: ", deaths))
# Set up mapping details
set_map_details <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('white')
)
# Make sure both maps are on the same color scale
shadeLimit <- 9
# Create the map
fig <- plot_geo(cv_CFR, locationmode = 'USA-states') %>% 
  add_trace(
    z = ~naive_CFR, text = ~hover, locations = ~state,
    color = ~naive_CFR, colors = 'Purples'
  )
fig <- fig %>% colorbar(title = "CFR May 1 2020", limits = c(0,shadeLimit))
fig <- fig %>% layout(
    title = paste('CFR by State as of', Sys.Date(), '<br>(Hover for value)'),
    geo = set_map_details
  )
fig_May1 <- fig
#############
### For Today
# Extract the data for each state by its abbreviation
cv_CFR <- cv_states_today %>%  select(state, abb, naive_CFR, cases, deaths) # select data
cv_CFR$state_name <- cv_CFR$state
cv_CFR$state <- cv_CFR$abb
cv_CFR$abb <- NULL
# Create hover text
cv_CFR$hover <- with(cv_CFR, paste(state_name, '<br>', "CFR: ", naive_CFR, '<br>', "Cases: ", cases, '<br>', "Deaths: ", deaths))
# Set up mapping details
set_map_details <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('white')
)
# Create the map
fig <- plot_geo(cv_CFR, locationmode = 'USA-states') %>% 
  add_trace(
    z = ~naive_CFR, text = ~hover, locations = ~state,
    color = ~naive_CFR, colors = 'Purples'
  )
fig <- fig %>% colorbar(title = "CFR May 1 2020", limits = c(0,shadeLimit))
fig <- fig %>% layout(
    title = paste('CFR by State as of', Sys.Date(), '<br>(Hover for value)'),
    geo = set_map_details
  )
fig_Today <- fig
### Plot together 
subplot(fig_May1, fig_Today)